Reappraising reappraisal: an expanded view
Bibliographic record
Abstract
Reappraisal is a frequently used and often successful emotion regulation strategy. However, its underlying cognitive mechanisms are not well understood. In this paper, we seek to clarify these mechanisms by expanding upon our recently proposed reAppraisal framework. According to this framework, reappraisal consists of appraisal shifts that arise from changes to the mental construal of a situation (reconstrual) or from changes to the goals that are used to evaluate the construal (repurposing). Here we propose that reappraisal can target both object-level construals and goals representing states in the environment as well as meta-level construals and goals about different states in the mind. We also propose that reappraisal can operate by facilitating decommitment from a dominant construal or goal as well as by facilitating commitment to alternative construals or goals. We demonstrate that the 2 × 2 × 2 matrix formed by crossing the three distinctions between reconstrual and repurposing, between object-level and meta-level representations, and between decommitment and commitment operations forms a useful map of different reappraisal tactics. We draw examples of each of the 8 reappraisal tactics from basic and clinical research. We conclude by considering future research inspired by the expanded reAppraisal framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".